Strain-level transmission inference across multi-kingdom metagenomic data using TRACS

Area of Science:

• Microbiology
• Genomics
• Bioinformatics

Background:

• Metagenomic data analysis is challenged by coexisting strains of the same species.
• Accurate inference of microbial transmission is crucial for understanding disease spread and community dynamics.

Purpose of the Study:

• To introduce TRAnsmision Clustering of Strains (TRACS), a novel algorithm for precise strain-level genetic distance estimation.
• To demonstrate TRACS's superior performance compared to existing methods in analyzing intra-species diversity.

Main Methods:

• Developed TRACS algorithm for estimating genetic distances at the single nucleotide polymorphism level.
• Validated TRACS using faecal microbiota transplantation datasets and extensive simulations.
• Applied TRACS to diverse datasets including SARS-CoV-2, Streptococcus pneumoniae, Plasmodium falciparum, and mother-infant gut metagenomes.

Main Results:

• TRACS significantly outperforms existing methods in accuracy and robustness to intra-species diversity.
• Inferred transmission networks in patients with multiple coexisting strains across different microbial kingdoms.
• Identified species-specific transmission rates and increased Bifidobacterium breve persistence in infants.

Conclusions:

• TRACS provides a powerful tool for uncovering microbial strain dynamics in complex communities.
• The algorithm enhances our ability to track pathogen and commensal microbe transmission.
• TRACS has broad applicability across various microbial sequencing datasets and research areas.